MDSF: Context-Aware Multi-Dimensional Data Storytelling Framework based on Large language Model

TL;DR

This paper presents MDSF, a framework leveraging large language models for automated, context-aware multidimensional data storytelling that improves insight discovery, narrative quality, and user engagement.

Contribution

The paper introduces a novel LLM-based framework with advanced preprocessing, augmented analysis, and real-time control for automated data storytelling.

Findings

MDSF outperforms existing methods in insight ranking accuracy.

It generates more coherent and detailed narratives.

User studies show increased satisfaction and utility.

Abstract

The exponential growth of data and advancements in big data technologies have created a demand for more efficient and automated approaches to data analysis and storytelling. However, automated data analysis systems still face challenges in leveraging large language models (LLMs) for data insight discovery, augmented analysis, and data storytelling. This paper introduces the Multidimensional Data Storytelling Framework (MDSF) based on large language models for automated insight generation and context-aware storytelling. The framework incorporates advanced preprocessing techniques, augmented analysis algorithms, and a unique scoring mechanism to identify and prioritize actionable insights. The use of fine-tuned LLMs enhances contextual understanding and generates narratives with minimal manual intervention. The architecture also includes an agent-based mechanism for real-time storytelling continuation control. Key findings reveal that MDSF outperforms existing methods across various datasets in terms of insight ranking accuracy, descriptive quality, and narrative coherence. The experimental evaluation demonstrates MDSF's ability to automate complex analytical tasks, reduce interpretive biases, and improve user satisfaction. User studies further underscore its practical utility in enhancing content structure, conclusion extraction, and richness of detail.